The Impact of Hurricane Strikes on Short-Term Local Economic Activity: Evidence from Nightlight Images in the Dominican Republic

Abstract

The Dominican Republic is highly exposed to adverse natural events that put the country at risk of losing hard-won economic, social, and environmental gains due to the impacts of disasters. This study used monthly nightlight composites in conjunction with a wind field model to econometrically estimate the impact of tropical cyclones on local economic activity in the Dominican Republic since 1992. It was found that the negative impact of storms lasts up to 15 months after a strike, with the largest effect observed after 9 months. Translating the reduction in nightlight intensity into monetary losses by relating it to quarterly gross domestic product (GDP) suggests that on average the storms reduced GDP by about USD 1.1 billion (4.5% of GDP 2000 and 1.5% of GDP 2016).

Keywords

Dominican Republic / Econometric analysis / Hurricanes / Nightlights

References

[1] Báez JE, Fuchs A, Rodríguez-Castelán C. Overview: Shaking up economic progress: Aggregate shocks in Latin America and the Caribbean, 2017, Washington, DC: World Bank

[2] Bertinelli L, Strobl E. Quantifying the local economic growth impact of hurricane strikes: An analysis from outer space for the Caribbean. Journal of Applied Meteorology and Climatology, 2013, 52(8): 1688-1697

[3] Bertinelli L, Mohan P, Strobl E. Hurricane damage risk assessment in the Caribbean: An analysis using synthetic hurricane events and nightlight imagery. Ecological Economics, 2016, 124: 135-144

[4] Bluhm, R., and M. Krause. 2016. Top lights: Bright spots and their contribution to economic development. https://editorialexpress.com/cgi-bin/conference/download.cgi?db_name=EEAESEM2016&paper_id=2519. Accessed 22 Aug 2019.

[5] Blundell R, Stoker TM. Heterogeneity and aggregation. Journal of Economic Literature, 2005, 43(2): 347-391

[6] Boose E, Mayra IS, David RF. Landscape and regional impacts of hurricanes in Puerto Rico. Ecological Monograph, 2004, 74(2): 335-352

[7] CEPAL (Comisión Económica para América Latina y el Caribe/Economic Commission for Latin America and the Caribbean) Dominican Republic post-disaster needs assessment of Hurricane Georges, 1998 (República Dominicana evaluación de las necesidades posteriores al desastre del huracán Georges, 1998), 1998, Mexico, D.F.: CEPAL (in Spanish)

[8] Chen X, Nordhaus W. Using luminosity data as a proxy for economic statistics. Proceedings of the National Academy of Sciences, 2011, 108(21): 8589-8594

[9] Doll CN. CIESIN thematic guide to night-time light remote sensing and its applications, 2008, Palisades, NY: Center for International Earth Science Information Network of Columbia University

[10] Driscoll JC, Kraay AC. Consistent covariance matrix estimation with spatially dependent panel data. Review of Economics and Statistics, 1998, 80(4): 549-560

[11] Elliott R, Strobl E, Sun P. The local impact of typhoons on economic activity in China: A view from outer space. Journal of Urban Economics, 2015, 88: 50-66

[12] Emanuel K. Global warming effects on U.S. hurricane damage. Weather, Climate, and Society, 2011, 3: 261-268

[13] Ferreira FHG, Messina J, Rigolini J, López-Calva L-F, Lugo MA, Vakis R. Economic mobility and the rise of the Latin American middle class, 2013, Washington, DC: World Bank

[14] Ghosh T, Anderson S, Elvidge C, Sutton P. Using nighttime satellite imagery as a proxy measure of human well-being. Sustainability, 2013, 5(12): 4988-5019

[15] Henderson JV, Storeygard A, Weil DN. Measuring economic growth from outer space. American Economic Review, 2012, 102(2): 994-1028

[16] Holland G. An analytic model of the wind and pressure profiles in hurricanes. Monthly Weather Review, 1980, 108(8): 1212-1218

[17] Holland G. A revised hurricane pressure–wind model. Monthly Weather Review, 2008, 136(9): 3432-3445

[18] Ishizawa, O.A.E., J.J.M. Montero, and H. Zhang. 2017. Understanding the impact of windstorms on economic activity from night lights in Central America. World Bank Policy Research Working Paper No. 8124. Washington, DC: World Bank.

[19] Jiang H, Halverson JB, Zipser EJ. Influence of environmental moisture on TRMM-derived tropical cyclone precipitation over land and ocean. Geophysical Research Letters, 2008, 35(17): 1-6

[20] Mellander C, Lobo J, Stolarick K, Matheson Z. Night-time light data: A good proxy measure for economic activity?. PloS ONE, 2015, 10(10): e0139779

[21] MEPyD (Ministerio de Economía, Planificación y Desarrollo y Banco Mundial/Ministry of Economy, Planning and Development) and World Bank. 2015. Financial management and disaster risk assurance in the Dominican Republic (Gestión Financiera y Aseguramiento del Riesgo de desastres en la Republica Dominicana). Dominican Republic.

[22] Mohan P, Strobl E. The short-term economic impact of tropical Cyclone Pam: An analysis using VIIRS nightlight satellite imagery. International Journal of Remote Sensing, 2017, 38(21): 5992-6006

[23] Noy, I., and W. duPont IV. 2016. The long-term consequences of natural disasters—A summary of the literature. SEF (School of Economics and Finance) Working Paper 02/2016. Wellington, NZ: Victoria University of Wellington. http://researcharchive.vuw.ac.nz/bitstream/handle/10063/4981/Working%20Paper.pdf?sequence=1. Accessed 21 May 2019.

[24] Paulsen BM, Schroeder JL. An examination of tropical and extratropical gust factors and the associated wind speed histograms. Journal of Applied Meteorology and Climatology, 2005, 44(2): 270-280

[25] Strobl E. The macroeconomic impact of natural disasters in developing countries: Evidence from hurricane strikes in the Central American and Caribbean region. Journal of Development Economics, 2012, 97: 130-141

[26] Taylor, M.A., L.A. Clarke, A. Centella, A. Bezanilla, T.S. Stephenson, J.J. Jones, and J. Charlery. 2018. Future Caribbean climates in a world of rising temperatures: The 1.5 vs 2.0 dilemma. Journal of Climate 31(7): 2907–2926.

[27] Vickery PJ, Wadhera D, Powell MD, Chen Y. A hurricane boundary layer and Wind Field Model for use in engineering applications. Journal of Applied Meteorology, 2009, 48(2): 381-405

[28] Villarini G, Vecchi GA. Twenty-first-century projections of North Atlantic tropical storms from CMIP5 models. Nature Climate Change, 2012, 2(8): 604

[29] Villarini G, Vecchi GA. Projected increases in North Atlantic tropical cyclone intensity from CMIP5 models. Journal of Climate, 2013, 26(10): 3231-3240

[30] Xiao Q, Zhang X, Davis C, Tuttle J, Holland G, Fitzpatrick PJ. Experiments of hurricane initialization with airborne Doppler radar data for the Advanced Research Hurricane WRF (AHW) model. Monthly Weather Review, 2009, 137(9): 2758-2777

[31] Yi K, Tani H, Li Q, Zhang J, Guo M, Bao Y, Wang X, Li J. Mapping and evaluating the urbanization process in northeast China using DMSP/OLS nighttime light data. Sensors, 2014, 14(2): 3207-3226